学生月上网时间分布-TestData

2024-08-21 05:23:49

Python机器学习应用 | 【第一周】无监督学习 - weixin_42906066的博客 - CSDN博客
https://blog.csdn.net/weixin_42906066/article/details/81867175

前置课程没有听，科学计算numpy和matlib画图（shuf）等在硬盘里，看一下会很方便进度。

=========

import numpy as np
import sklearn.cluster as skc
from sklearn import metrics
import matplotlib.pyplot as plt
mac2id=dict()
onlinetimes=[]
f=open('TestData.txt',encoding='utf-8')
for line in f:
mac=line.split(',')[2]
onlinetime=int(line.split(',')[6])
starttime=int(line.split(',')[4].split(' ')[1].split(':')[0])
if mac not in mac2id:
mac2id[mac]=len(onlinetimes)
onlinetimes.append((starttime,onlinetime))
else:
onlinetimes[mac2id[mac]]=[(starttime,onlinetime)]
real_X=np.array(onlinetimes).reshape((-1,2))
X=real_X[:,0:1]
db=skc.DBSCAN(eps=0.01,min_samples=20).fit(X)
labels = db.labels_
print('Labels:')
print(labels)
raito=len(labels[labels[:] == -1]) / len(labels)
print('Noise raito:',format(raito, '.2%'))
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_clusters_)
print("Silhouette Coefficient: %0.3f"% metrics.silhouette_score(X, labels))
for i in range(n_clusters_):
print('Cluster ',i,':')
print(list(X[labels == i].flatten()))
plt.hist(X,24)

最新文章

热门文章